Generalized AutoRegressive Conditional Heteroskedasticity Model - GARCH - Economics Assignment Help

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Introduction of Garch Model --> definition, how can it be useful. what it tests Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) is a statistical model used in analyzing time-series data where the variance error is believed to be serially autocorrelated. GARCH models assume that the variance of the error term follows an autoregressive moving average process 1. In other words, it illustrates the relationship between the conditional variance and its own previous lags over the sample periods of time. In Finance, the model is more popular over other known models, and it is used to predict financial instruments future price in a way of its returns, and also taking into account their volatility. The GARCH model’s popularism is based on its possibility to avoid over-fitting, which is usually described as parsimonious. In two words, parsimony is when the given model tries to explain the main dependent variable with the most possible fewest independent variables. And one more difference of the GARCH model is that other similar models’ constraint to assess volatility, in which GARCH good at by taking it as non-constant. This explains the model’s wide usage in risk-management and option pricing.

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